p1 class demo

Author

Greeshma

Develop an R program to quickly explore a given dataset, including categorical analysis using the group_by command , and visualize the findings using ggplot2 features.

step 1:Load necessary libraries

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dplyr)

Step 2:Load the dataset

# Load dataset
data<- mtcars
# convert 'cyl' to a factor for categorical analysis
data$cyl <- as.factor(data$cyl)

Step 3:Group by categorical variables

#summarize avg mpg by cylinder category 
summary_data <- data %>%
  group_by (cyl) %>%
  summarise(avg_mpg = mean(mpg), .groups = 'drop')

#display summary
print(summary_data)

# A tibble: 3 × 2
  cyl   avg_mpg
  <fct>   <dbl>
1 4        26.7
2 6        19.7
3 8        15.1

Step 4: Visualizing the findings

#create a bar plot using ggplot2
ggplot(summary_data ,aes(x= cyl,y = avg_mpg, fill= cyl)) + 
  geom_bar(stat = "identity") +
  labs(title = "Average MPG by cylinder count" ,
       x="number of cylindesrs",
       y= "average MPG") +
  theme_minimal()